Data Science is a multi-disciplinary practice which includes business, engineering, analysis, modeling and communication. This presentation is about what are the most important aspects to focus on at each of these steps.

1. Chief Data Scientist,ADP

2. amit_sharma_ai
amit-sharma-ai

3. Payroll Tax Time HR Talent Benefits
• Small, Midsized, Large, Multinational markets
• Revenues: $11.7 billion in fiscal 2016
• ADP pays 24 million (1 in 6) workers in U.S., and 12 million
elsewhere
• FORTUNE 500®: Ranked 240
• Forbes® Global 2000: Ranked 334
• Human Capital Management
ADP

5. Ask a
question
(Leader)
Source
the data
(Engg)
Explore
data
(Analyst)
Model
data
(Scientist)
Share
Results
(Scientist)

6. (Binary Classification)
(Multi-class classification)
(Anamoly detection)
(Regression)
(Unsupervised)
(Reinforcement)

7. Ask a
question
What’s my
Company &
Product’s
Goal/Vision?
How can we
make our
products
‘smarter’?
What data do
we have or
can get?
Will this be
useful to end
user?
Business/Domain Expert
DB and Data Expert

8. Data
Security
Legal
Velocity
Granularity

9. Explore distributions
• Are they telling a story
Handle missing data
• Can the missing data be ignored
• Does it need to be imputed
Look for outliers
• Do we want to
identify/filter/manage them

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18
1-3 4-6 7-10 11-14 15-18 19-22 23-26 27-30
No of Questions
%Dropout
0
2
4
6
8
10
12
14
16
18
0 100 200 300 400
%Dropout
No of Questions
How removing outliers helped uncover the correlation

12. Bias (underfitting) Optimal Variance (overfitting)
Solution
• Add more features
• Use a more complex model
Solution
• Fewer features
• More data to reduce
variance

13. overall accuracy is not good enough
People
resigned
People Not
Resigned
High Risk 30
100
(Type 1 Error)
Low Risk
70
(Type 2 Error)
800
OverallAccuracy – 83%
Sensitivity (Recall) – 30%

17. Intellectually
Curious
Domain
Knolwedge
Great
Communications
Statistical
Knowledge
Hands-on
Programming

18. Learn
Online and
Curriculum
Compete
Analytics
Challenges
Explore
Datasets
Follow
Trend/Tech
Go Deep
Network
Share and Learn
Forums, Meetups

19. Ask a
question
(Leader)
Source
the data
(Engg)
Explore
data
(Analyst)
Model
data
(Scientist)
Share
Results
(Scientist)
- How can I use data to
drive value for my
users/customers?
- How to ensure
Legality, Security, IP of
data?
- Explore distributions
- Handle missing values
and outliers
- Keep it Simple
- Progressive Disclosure
- Prefer simple, explicable,
actionable models
- Avoid over/under fitting
- Validation is key